1. Introduction
Poisson process is used to model the occurrences of events and the time points at which the events occur in a given time interval, such as the occurrence of natural disasters and the arrival times of customers at a service center. It is named after the French mathematician Siméon Poisson (1781-1840). In this paper, we first give the definition of the Poisson process (Section 2). Then we stated some theroems related to the Poisson process (Section 3). Finally, we give some examples and compute the relevant quantities associated with the process (Section 4).
2. What Is Poisson Process?
A Poisson process with parameter (rate)
is a family of random variables
satisfying the following properties:
1)
.
2)
are independent random variables where
.
3)
for
.
can be thought of the number of arrivals up to time t or the number of occurrences up to time t.
3. Some Facts about the Poisson Process
We give some properties associated with the Poisson process. The proofs can be found in [1] or [2] . If we let
be the time of the
arrival
, and we let
,
, be the interarrival time
. Then we have the following theorems:
Theorem 1 The
arrival time has the
-distribution with density function
, for ![]()
Theorem 2 The interarrival times
are independently exponentially distributed random variables with parameter
.
Theorem 3 Conditioned on
, the random variables
have the joint density probability function
![]()
Theorem 4 If
is a random variable associated with the
event in a Poisson process with parameter
. We assume that
are independent, independent of the Poisson process, and share the common distribution function
. The sequence of pairs
is called a marked Poisson process. The
form a two-dimensional nonhomogeneous Poisson point process in the
plane, where the mean number of points in a region
is given by
![]()
The marked Poisson processes have been applied in some geometric probability area [3] .
4. Examples of Poisson Processes
1) Suppose the number of calls to a phone number is a Poisson process
with parameter
and
is the duration of each call. It is reasonable to assume that
is independent of the Poisson process. What is the probability
that the
call gets a busy signal, i.e. it comes when the user is still responding to the
call?
For a fixed
,
![]()
![]()
2) On average, how many calls arrive when the user is on the phone?
Suppose the user is talking on the
call,
![]()
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3) In a single server system, customers arrive in a bank according to a Poisson process with parameter
and each customer spends
time with the one and only one bank teller. If the teller is serving a customer, the new customers have to wait in a queue till the teller finishes serving. How long on average does the teller serves the customers up to time
? (i.e. How long is the server unavailable?)
![]()
4) Suppose team A and team B are engaging in a sport competition. The points scored by team A follows a Poisson process
with parameter
and the points scored by team B follows a Poisson process
with parameter
. Assume that
and
are independent, what is the probability that the game ties? Team A wins? Team B wins?
Let
be the duration of the competition.
![]()
![]()
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5) Given that there are
points scored in a match (by both team A and team B), what is the probability that team A scores
points, where
?
![]()
6) When does a car accident happen? Suppose a street is from west to east and another is from south to north, the two streets intersect at a point
. Cars going from west to east arrives at
follows a Poisson process
with parameter
and cars going from south to east arrives at
follows a Poisson process
with parameter
. It is reasonable to assume that these two processes are independent. If the cars don’t slow down and stop at the intersection
, then collision happens. The
car going from south to north hits the
car going from south to east if and only if
, where
is the time it takes for the car's tail to reach
,
has density function
.
![]()
![]()
![]()
7) Occurrences of natural disasters follow a Poisson process with parameter
. Suppose that the time it takes to recover and rebuild after the
disaster is
, assume that
are independent random variables having the common distribution functions
. There are
disasters up to time
, what is the probability that everything is back to normal at time
? This can also be used as a model for insurance claims.
is the time for the insurance company to receive the
claim and
is the time the insurance company takes to settle it. What is the probability that the insurance company is not working on any claim at time
?
![]()
where
are independent and uniformly distributed on
.
![]()
8) Suppose that
is the time an insurance company receives the
claim and
is the time the company takes to settle the claim. What is the average time to settle all claims received before time
?
The average time to settle all claims received before
is
![]()
Suppose
,
![]()
where
are independent and uniformly distributed on
.
![]()
Clearly,
for
.
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9) Customers arrive at a shopping mall follows a Poisson process with parameter
. The time the customers spend in the store
are independent random variables having the common distribution function
. Let
be the number of customers exist up to the closing time
. What is the expected number of customers in the mall at time
?
Condition on
and let
be the arrival time of the customers. Then customer
exists in the mall at time
if and only if
. Let the random variable
![]()
Then
if and only if the
customer exists in the mall at time
. Thus
![]()
where
are independent and uniformly distributed on
.
is the binomial distribution in which
![]()
Hence,
![]()
That is, the number of customers existing at time
has a Poisson distribution with mean
![]()
The average number of customers exist at the mall closing time is
![]()
10) Customers arriving at a service counter follows a Poisson process with parameter
. Let
be the number of customers served longer than
up to time
. What is the distribution of
?
Condition on
and let
be the arrival time of the customers. Let the random variable
![]()
Then
if and only if the
customer served longer than
. Thus
![]()
which is the binomial distribution with
. Hence,
![]()
That is, the number of customers served longer than
has a Poisson distribution with mean
![]()
5. Conclusion
Poisson process is one of the most important tools to model the natural phenomenon. Some important distributions arise from the Poisson process: the Poisson distribution, the exponential distribution and the Gamma distribution. It is also used to build other sophisticated random process.